This paper presents a finite-time tracking control for a robot manipulator in the presence of a modeling uncertainty and an external disturbance. To solve the large chattering phenomenon that is caused by the high switching gain of the slidingmode control, a novel second-order sliding-mode controller that generates a continuous control input is designed with a robust differentiator. The finite-time stability of the closed-loop system is ensured using a constructive Lyapunov-stability analysis. Finally, a numerical simulation of the 2-Axis Pan-Tilt system is performed to verify the effectiveness of the proposed controller.

2-Axis Pan and Tilt Motion Platform, a complex multivariate non-linear system, may incur any disturbance, thus requiring system controller with robustness against various disturbances. In this study, we designed an adaptive backstepping compensated controller by estimating the disturbance and error using the Radial Basis Function Neural Network (RBF NN). In this process, Uniformly Ultimately Bounded (UUB) was demonstrated via Lyapunov and stability was confirmed. By generating progressive disturbance to the irregular frequency and amplitude changes, it was verified for various environmental disturbances. In addition, by setting the RBF NN input vector to the minimum, the estimated disturbance compensation process was analyzed. Only two input vectors facilitated compensatory function of RBF NN via estimating the modeling and control error values as well as irregular disturbance; the application of the process resulted in improved backstepping controller performance that was confirmed through simulation.